Analysis of House Price Data

#import numpy
import numpy as np
#import pandas
import pandas as pd
#import seaborn
import seaborn as sns
# import matplotlib.pyplot
import matplotlib.pyplot as plt

# import the data set
df_house_price_data = pd.read_csv("C:\\Users\\jemin\\OneDrive\\Desktop\\SEM 6\\Pythonscript RNGPIT\\House_Price.csv")

 # show the first observation of the dataset
df_house_price_data.head()

	price	crime_rate	resid_area	air_qual	room_num	age	dist1	dist2	dist3	dist4	teachers	poor_prop	airport	n_hos_beds	n_hot_rooms	waterbody	rainfall	bus_ter	parks
0	24.0	0.00632	32.31	0.538	6.575	65.2	4.35	3.81	4.18	4.01	24.7	4.98	YES	5.480	11.1920	River	23	YES	0.049347
1	21.6	0.02731	37.07	0.469	6.421	78.9	4.99	4.70	5.12	5.06	22.2	9.14	NO	7.332	12.1728	Lake	42	YES	0.046146
2	34.7	0.02729	37.07	0.469	7.185	61.1	5.03	4.86	5.01	4.97	22.2	4.03	NO	7.394	101.1200	None	38	YES	0.045764
3	33.4	0.03237	32.18	0.458	6.998	45.8	6.21	5.93	6.16	5.96	21.3	2.94	YES	9.268	11.2672	Lake	45	YES	0.047151
4	36.2	0.06905	32.18	0.458	7.147	54.2	6.16	5.86	6.37	5.86	21.3	5.33	NO	8.824	11.2896	Lake	55	YES	0.039474

# show last five observation of the dataset
df_house_price_data.tail()

	price	crime_rate	resid_area	air_qual	room_num	age	dist1	dist2	dist3	dist4	teachers	poor_prop	airport	n_hos_beds	n_hot_rooms	waterbody	rainfall	bus_ter	parks
501	22.4	0.06263	41.93	0.573	6.593	69.1	2.64	2.45	2.76	2.06	19.0	9.67	NO	9.348	12.1792	Lake and River	27	YES	0.056006
502	20.6	0.04527	41.93	0.573	6.120	76.7	2.44	2.11	2.46	2.14	19.0	9.08	YES	6.612	13.1648	Lake and River	20	YES	0.059903
503	23.9	0.06076	41.93	0.573	6.976	91.0	2.34	2.06	2.29	1.98	19.0	5.64	NO	5.478	12.1912	None	31	YES	0.057572
504	22.0	0.10959	41.93	0.573	6.794	89.3	2.54	2.31	2.40	2.31	19.0	6.48	YES	7.940	15.1760	None	47	YES	0.060694
505	19.0	0.04741	41.93	0.573	6.030	80.8	2.72	2.24	2.64	2.42	19.0	7.88	YES	10.280	10.1520	None	45	YES	0.060336

1. Data Overview

# Show the Attribute Names
df_house_price_data.columns

Index(['price', 'crime_rate', 'resid_area', 'air_qual', 'room_num', 'age',
       'dist1', 'dist2', 'dist3', 'dist4', 'teachers', 'poor_prop', 'airport',
       'n_hos_beds', 'n_hot_rooms', 'waterbody', 'rainfall', 'bus_ter',
       'parks'],
      dtype='object')

# Price=Price of house # Crime_rate= crime rate around the house # resid_area=Residence Area around the House # air_qual=Air quality around the House # room_num=Number o Room in the House # age= Age of House (How many yers old of House) # dist1= Distance from industrial area1 # dist2= Distance from industrial area2 # dist3= Distance from industrial area3 # dist4= Distance from industrial area4 # teachers=Howmany Teacher Stay in Aroud the House # poor_prop= Poor Population in Around the House # airport= airport Available or not Aroud the House # n_hos_beds= Numbers bads in the Hospital Around the House # n_hot_rooms= Numbers room in the Hotels Around the House # waterbody= Resource of Water Around the House # rainfall= mm rate of rainfall Around the House # bus_ter = Bus Terminal Availble in Around the House # parks= Houmany parks

colomninfo -price:Price of the House -Crime_rate:crime rate around the house

Basic Statistics(Discriptive Statistics)

# Shape of the Data 
# Basic Info
# Basic Discription

# Shape of the Data
df_house_price_data.shape

(506, 19)

Interpritetion

• We have 506 observation and 19 attributes

# Basic Info(function)
df_house_price_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 19 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   price        506 non-null    float64
 1   crime_rate   506 non-null    float64
 2   resid_area   506 non-null    float64
 3   air_qual     506 non-null    float64
 4   room_num     506 non-null    float64
 5   age          506 non-null    float64
 6   dist1        506 non-null    float64
 7   dist2        506 non-null    float64
 8   dist3        506 non-null    float64
 9   dist4        506 non-null    float64
 10  teachers     506 non-null    float64
 11  poor_prop    506 non-null    float64
 12  airport      506 non-null    object 
 13  n_hos_beds   498 non-null    float64
 14  n_hot_rooms  506 non-null    float64
 15  waterbody    506 non-null    object 
 16  rainfall     506 non-null    int64  
 17  bus_ter      506 non-null    object 
 18  parks        506 non-null    float64
dtypes: float64(15), int64(1), object(3)
memory usage: 75.2+ KB

Interpritation

• We have 16 numarical Attributes its Includes 15 Float And 1 intigers
• We have 3 Catagorical attributes
• In the Data We Have valid null values

# Basic Discription
df_house_price_data.describe().T

	count	mean	std	min	25%	50%	75%	max
price	506.0	22.528854	9.182176	5.000000	17.025000	21.200000	25.000000	50.000000
crime_rate	506.0	3.613524	8.601545	0.006320	0.082045	0.256510	3.677083	88.976200
resid_area	506.0	41.136779	6.860353	30.460000	35.190000	39.690000	48.100000	57.740000
air_qual	506.0	0.554695	0.115878	0.385000	0.449000	0.538000	0.624000	0.871000
room_num	506.0	6.284634	0.702617	3.561000	5.885500	6.208500	6.623500	8.780000
age	506.0	68.574901	28.148861	2.900000	45.025000	77.500000	94.075000	100.000000
dist1	506.0	3.971996	2.108532	1.130000	2.270000	3.385000	5.367500	12.320000
dist2	506.0	3.628775	2.108580	0.920000	1.940000	3.010000	4.992500	11.930000
dist3	506.0	3.960672	2.119797	1.150000	2.232500	3.375000	5.407500	12.320000
dist4	506.0	3.618972	2.099203	0.730000	1.940000	3.070000	4.985000	11.940000
teachers	506.0	21.544466	2.164946	18.000000	19.800000	20.950000	22.600000	27.400000
poor_prop	506.0	12.653063	7.141062	1.730000	6.950000	11.360000	16.955000	37.970000
n_hos_beds	498.0	7.899767	1.476683	5.268000	6.634500	7.999000	9.088000	10.876000
n_hot_rooms	506.0	13.041605	5.238957	10.057600	11.189800	12.720000	14.170800	101.120000
rainfall	506.0	39.181818	12.513697	3.000000	28.000000	39.000000	50.000000	60.000000
parks	506.0	0.054454	0.010632	0.033292	0.046464	0.053507	0.061397	0.086711

Interpritation

• We have outlier in crime_rate,age,resid_area,teachers,poor_prop

2. Data Pre-Processing

# Data Cleaning
# null Values Handling
# outlier Treatement

Data Cleaning

# Data Cleaning
df_house_price_data.columns

Index(['price', 'crime_rate', 'resid_area', 'air_qual', 'room_num', 'age',
       'dist1', 'dist2', 'dist3', 'dist4', 'teachers', 'poor_prop', 'airport',
       'n_hos_beds', 'n_hot_rooms', 'waterbody', 'rainfall', 'bus_ter',
       'parks'],
      dtype='object')

# lets rename the columns
df_house_price_data=df_house_price_data.rename(columns={'price':'Price','crime_rate':'Crime_Rate','resid_area':'Resid_Area',
'air_qual':'Air_Quality','room_num':'Room_Num','age':'Age',
'dist1':'Dist1','dist2':'Dist2','dist3':'Dist3','dist4':'Dist4',
'teachers':'Teachers','poor_prop':'Poor_Population','airport':'Airport',
'n_hos_beds':'N_Hos_Beds','n_hot_rooms':'N_Hot_Rooms',
'waterbody':'Waterbody','rainfall':'Rainfall',
'bus_ter':'Bus_Terminal','parks':'Parks'})

df_house_price_data.head()

	Price	Crime_Rate	Resid_Area	Air_Quality	Room_Num	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	Airport	N_Hos_Beds	N_Hot_Rooms	Waterbody	Rainfall	Bus_Terminal	Parks
0	24.0	0.00632	32.31	0.538	6.575	65.2	4.35	3.81	4.18	4.01	24.7	4.98	YES	5.480	11.1920	River	23	YES	0.049347
1	21.6	0.02731	37.07	0.469	6.421	78.9	4.99	4.70	5.12	5.06	22.2	9.14	NO	7.332	12.1728	Lake	42	YES	0.046146
2	34.7	0.02729	37.07	0.469	7.185	61.1	5.03	4.86	5.01	4.97	22.2	4.03	NO	7.394	101.1200	None	38	YES	0.045764
3	33.4	0.03237	32.18	0.458	6.998	45.8	6.21	5.93	6.16	5.96	21.3	2.94	YES	9.268	11.2672	Lake	45	YES	0.047151
4	36.2	0.06905	32.18	0.458	7.147	54.2	6.16	5.86	6.37	5.86	21.3	5.33	NO	8.824	11.2896	Lake	55	YES	0.039474

# Let's Do the Typecasting Here
# Room_num
# Age
# Techers
# N_Hos_Beds
# N_Hot_Rooms

df_house_price_data['Room_Num']=df_house_price_data['Room_Num'].astype('int')

df_house_price_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 19 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Price            506 non-null    float64
 1   Crime_Rate       506 non-null    float64
 2   Resid_Area       506 non-null    float64
 3   Air_Quality      506 non-null    float64
 4   Room_Num         506 non-null    int32  
 5   Age              506 non-null    float64
 6   Dist1            506 non-null    float64
 7   Dist2            506 non-null    float64
 8   Dist3            506 non-null    float64
 9   Dist4            506 non-null    float64
 10  Teachers         506 non-null    float64
 11  Poor_Population  506 non-null    float64
 12  Airport          506 non-null    object 
 13  N_Hos_Beds       498 non-null    float64
 14  N_Hot_Rooms      506 non-null    float64
 15  Waterbody        506 non-null    object 
 16  Rainfall         506 non-null    int64  
 17  Bus_Terminal     506 non-null    object 
 18  Parks            506 non-null    float64
dtypes: float64(14), int32(1), int64(1), object(3)
memory usage: 73.3+ KB

df_house_price_data['Age']=df_house_price_data['Age'].astype('int')

df_house_price_data['Teachers ']=df_house_price_data['Teachers'].astype('int')

df_house_price_data['N_Hos_Beds']=df_house_price_data['N_Hos_Beds '].astype('int')

df_house_price_data['N_Hot_Rooms']=df_house_price_data['N_Hot_Rooms'].astype('int')

# in Previous We did the typecasting base the on observation and Domain expertise
# Now time is for Data visulization 
df_house_price_data.hist()
plt.tight_layout()
plt.show()

df_house_price_data.plot(kind= 'kde')

<Axes: ylabel='Density'>

df_house_price_data.Price.plot(kind='kde')
plt.tight_layout()
plt.show()

# Room_Num
# N_Hot_Rooms

df_house_price_data.Room_Num.hist(color='red')
plt.tight_layout()
plt.show()

df_house_price_data.N_Hot_Rooms.hist(color='red')
plt.tight_layout()
plt.show()

df_house_price_data.Room_Num.plot(kind='kde')
plt.tight_layout()
plt.show()

df_house_price_data.N_Hot_Rooms.plot(kind='kde')
plt.tight_layout()
plt.show()

df_house_price_data['Room_Num']=df_house_price_data['Room_Num'].astype('object')

df_house_price_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 20 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Price            506 non-null    float64
 1   Crime_Rate       506 non-null    float64
 2   Resid_Area       506 non-null    float64
 3   Air_Quality      506 non-null    float64
 4   Room_Num         506 non-null    object 
 5   Age              506 non-null    int32  
 6   Dist1            506 non-null    float64
 7   Dist2            506 non-null    float64
 8   Dist3            506 non-null    float64
 9   Dist4            506 non-null    float64
 10  Teachers         506 non-null    float64
 11  Poor_Population  506 non-null    float64
 12  Airport          506 non-null    object 
 13  N_Hos_Beds       498 non-null    float64
 14  N_Hot_Rooms      506 non-null    int32  
 15  Waterbody        506 non-null    object 
 16  Rainfall         506 non-null    int64  
 17  Bus_Terminal     506 non-null    object 
 18  Parks            506 non-null    float64
 19  Teachers         506 non-null    int32  
dtypes: float64(12), int32(3), int64(1), object(4)
memory usage: 73.3+ KB

# in Previous We did the typecasting base the on observation and Domain expertise
# Now time is for Data visulization 
df_house_price_data.hist()
plt.tight_layout()
plt.show()

Null Values Handling

df_house_price_data.head()

	Price	Crime_Rate	Resid_Area	Air_Quality	Room_Num	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	Airport	N_Hos_Beds	N_Hot_Rooms	Waterbody	Rainfall	Bus_Terminal	Parks	Teachers
0	24.0	0.00632	32.31	0.538	6	65	4.35	3.81	4.18	4.01	24.7	4.98	YES	5.480	11	River	23	YES	0.049347	24
1	21.6	0.02731	37.07	0.469	6	78	4.99	4.70	5.12	5.06	22.2	9.14	NO	7.332	12	Lake	42	YES	0.046146	22
2	34.7	0.02729	37.07	0.469	7	61	5.03	4.86	5.01	4.97	22.2	4.03	NO	7.394	101	None	38	YES	0.045764	22
3	33.4	0.03237	32.18	0.458	6	45	6.21	5.93	6.16	5.96	21.3	2.94	YES	9.268	11	Lake	45	YES	0.047151	21
4	36.2	0.06905	32.18	0.458	7	54	6.16	5.86	6.37	5.86	21.3	5.33	NO	8.824	11	Lake	55	YES	0.039474	21

# Find the number of Missing Values
df_house_price_data.isnull().sum()

Price              0
Crime_Rate         0
Resid_Area         0
Air_Quality        0
Room_Num           0
Age                0
Dist1              0
Dist2              0
Dist3              0
Dist4              0
Teachers           0
Poor_Population    0
Airport            0
N_Hos_Beds         8
N_Hot_Rooms        0
Waterbody          0
Rainfall           0
Bus_Terminal       0
Parks              0
Teachers           0
dtype: int64

df_house_price_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 20 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Price            506 non-null    float64
 1   Crime_Rate       506 non-null    float64
 2   Resid_Area       506 non-null    float64
 3   Air_Quality      506 non-null    float64
 4   Room_Num         506 non-null    object 
 5   Age              506 non-null    int32  
 6   Dist1            506 non-null    float64
 7   Dist2            506 non-null    float64
 8   Dist3            506 non-null    float64
 9   Dist4            506 non-null    float64
 10  Teachers         506 non-null    float64
 11  Poor_Population  506 non-null    float64
 12  Airport          506 non-null    object 
 13  N_Hos_Beds       498 non-null    float64
 14  N_Hot_Rooms      506 non-null    int32  
 15  Waterbody        506 non-null    object 
 16  Rainfall         506 non-null    int64  
 17  Bus_Terminal     506 non-null    object 
 18  Parks            506 non-null    float64
 19  Teachers         506 non-null    int32  
dtypes: float64(12), int32(3), int64(1), object(4)
memory usage: 73.3+ KB

# Laws
## 1.en We HAve 10% Data Missing remove the observation 
## 2.en We have persentage beetween 30 to 70 percent then we neet to inputs the missing values
## 3.en we have more than 80% remove the Attributes

round(df_house_price_data.isnull().sum()/len(df_house_price_data)*100,2)

Price              0.00
Crime_Rate         0.00
Resid_Area         0.00
Air_Quality        0.00
Room_Num           0.00
Age                0.00
Dist1              0.00
Dist2              0.00
Dist3              0.00
Dist4              0.00
Teachers           0.00
Poor_Population    0.00
Airport            0.00
N_Hos_Beds         1.58
N_Hot_Rooms        0.00
Waterbody          0.00
Rainfall           0.00
Bus_Terminal       0.00
Parks              0.00
Teachers           0.00
dtype: float64

#  We Have LEss tha 2% Data is Missing From N_Hos_Beds,Lets the Remove the observation 

df_house_price_data.dropna(inplace=True)

df_house_price_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 498 entries, 0 to 505
Data columns (total 20 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Price            498 non-null    float64
 1   Crime_Rate       498 non-null    float64
 2   Resid_Area       498 non-null    float64
 3   Air_Quality      498 non-null    float64
 4   Room_Num         498 non-null    object 
 5   Age              498 non-null    int32  
 6   Dist1            498 non-null    float64
 7   Dist2            498 non-null    float64
 8   Dist3            498 non-null    float64
 9   Dist4            498 non-null    float64
 10  Teachers         498 non-null    float64
 11  Poor_Population  498 non-null    float64
 12  Airport          498 non-null    object 
 13  N_Hos_Beds       498 non-null    float64
 14  N_Hot_Rooms      498 non-null    int32  
 15  Waterbody        498 non-null    object 
 16  Rainfall         498 non-null    int64  
 17  Bus_Terminal     498 non-null    object 
 18  Parks            498 non-null    float64
 19  Teachers         498 non-null    int32  
dtypes: float64(12), int32(3), int64(1), object(4)
memory usage: 75.9+ KB

df_house_price_data.isnull().sum()

Price              0
Crime_Rate         0
Resid_Area         0
Air_Quality        0
Room_Num           0
Age                0
Dist1              0
Dist2              0
Dist3              0
Dist4              0
Teachers           0
Poor_Population    0
Airport            0
N_Hos_Beds         0
N_Hot_Rooms        0
Waterbody          0
Rainfall           0
Bus_Terminal       0
Parks              0
Teachers           0
dtype: int64

outlier Detection and tritetment

df_house_price_data.plot(kind='box')
plt.xticks(rotation=90)
plt.title('Outlier Detection')
plt.tight_layout()
plt.show()

df_num=df_house_price_data.select_dtypes(np.number)

# Find limit
Q1=df_num.quantile(0.25)
Q3=df_num.quantile(0.75)
IQR=Q3-Q1
df_num=df_num[(df_num>=Q1-1.5*IQR)& (df_num<=Q3+1.5*IQR)]

df_num.reset_index()
df_num=df_num.reset_index()

df_num.plot(kind='box',color='red',rot=90)
plt.show()

# concate the tretment dataframe with catacgoriacl data frame
df_cat=df_house_price_data.select_dtypes('object')

df_cat=df_cat.reset_index()

print(df_cat.shape)

(498, 5)

print(df_num.shape)

(498, 17)

# concate both
df_house_price_data=pd.concat((df_num,df_cat),axis=1)
df_house_price_data.shape

(498, 22)

df_house_price_data.isnull().sum()

index               0
Price              39
Crime_Rate         64
Resid_Area          0
Air_Quality         0
Age                 0
Dist1               5
Dist2               5
Dist3               5
Dist4               5
Teachers           14
Poor_Population     7
N_Hos_Beds          0
N_Hot_Rooms         2
Rainfall            0
Parks               5
Teachers           14
index               0
Room_Num            0
Airport             0
Waterbody           0
Bus_Terminal        0
dtype: int64

df_house_price_data.dropna(inplace=True)

df_house_price_data.isnull().sum()

index              0
Price              0
Crime_Rate         0
Resid_Area         0
Air_Quality        0
Age                0
Dist1              0
Dist2              0
Dist3              0
Dist4              0
Teachers           0
Poor_Population    0
N_Hos_Beds         0
N_Hot_Rooms        0
Rainfall           0
Parks              0
Teachers           0
index              0
Room_Num           0
Airport            0
Waterbody          0
Bus_Terminal       0
dtype: int64

3.Expolotry Data Analysis

# Uni variant Analysis
# bivarint Analysis
# multivariant Analysis 

Uni variant Analysis

# lets Segrigates The  Numarical & Catagorical Columns
df_num =df_house_price_data.select_dtypes(include=np.number)

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

# Catagorical data frame
df_cat=df_house_price_data.select_dtypes(include=['object'])
df_cat.head()

	Room_Num	Airport	Waterbody	Bus_Terminal
0	6	YES	River	YES
1	6	NO	Lake	YES
3	6	YES	Lake	YES
4	7	NO	Lake	YES
5	6	YES	None	YES

Room_Num

# FInd The Count Of Each Catagary Present in that Particular Columns
df_cat.Room_Num.value_counts()

6    228
5    120
7     22
4      4
8      1
3      1
Name: Room_Num, dtype: int64

# Visulies the Count
df_cat.Room_Num.value_counts().plot(kind='bar', rot=360)
plt.show()

Interpritation

• We have imbalance data in Room_Num Column
• 6 is Catagary Who is Dominating is More, its counts is 266
• 3 is A Catagary Havind list numbers of Observation ,its counts is 2
• 8 and 4 are catagary Having same Number of Observation

df_cat.head()

	Room_Num	Airport	Waterbody	Bus_Terminal
0	6	YES	River	YES
1	6	NO	Lake	YES
3	6	YES	Lake	YES
4	7	NO	Lake	YES
5	6	YES	None	YES

Airport

# Find the Values Counts for Airport Column
df_cat.Airport.value_counts()

YES    205
NO     171
Name: Airport, dtype: int64

# Plot the Values Counts
df_cat.Airport.value_counts().plot(kind='bar')
plt.show()

Interpretation

• We Have balecnce Data in this Airport Column
• In The Data 273 Palces Where Airport is Present
• In The Data 225 Palces Where Airport isnot Present

df_cat.head(2)

	Room_Num	Airport	Waterbody	Bus_Terminal
0	6	YES	River	YES
1	6	NO	Lake	YES

Waterbody

# FInd the Count the Each catagary in the that column
df_cat.Waterbody.value_counts()

River             139
None              112
Lake               70
Lake and River     55
Name: Waterbody, dtype: int64

# visulize the count
df_cat.Waterbody.value_counts().plot(kind='bar',color='pink')
plt.title('Count of Waterbody')
plt.ylabel('counts')
plt.xlabel('Waterbody')
plt.xticks(rotation=45)
plt.grid()
plt.show()

Interpretation

• Most of the Palces having River as Water body ,its counts is 178
• where is few Palces lacks and river are the waterbody
• The Data is Imbalace
• 155 Palce are not Avalable waterbody

df_cat.head()

	Room_Num	Airport	Waterbody	Bus_Terminal
0	6	YES	River	YES
1	6	NO	Lake	YES
3	6	YES	Lake	YES
4	7	NO	Lake	YES
5	6	YES	None	YES

Bus_Terminal

# Find the Values Counts for Bus_Terminal Column
df_cat.Bus_Terminal.value_counts()

YES    376
Name: Bus_Terminal, dtype: int64

# visulize the count
df_cat.Bus_Terminal.value_counts().plot(kind='bar',color='pink')
plt.title('Count of Bus_Terminal')
plt.ylabel('counts')
plt.xlabel('Bus_Terminal')
plt.xticks(rotation=45)
plt.grid()
plt.show()

InterPretation

• We Have observed 498 Bus_Terminal Avalbale
• This Data is Balance Data
• All The Places we have , contains the bus terminal
• ACTION : that colums from the data

# Drop the column
df_house_price_data.drop('Bus_Terminal',axis=1,inplace=True)

df_house_price_data.shape

(376, 21)

df_house_price_data.shape[0]

376

df_house_price_data.shape[1]

21

Uni variant Analysis on Numarical Data

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Price

df_num.Price.plot(kind='kde',color='red')
plt.title('kde Plot of Price')
plt.grid()
plt.show()

# find the minimum values
df_num.Price.min()

7.0

# find the maximum values
df_num.Price.max()

36.4

# find the Average Values
df_num.Price.mean()

21.846808510638297

Interpretation

• This Data slidely Normaly Distributated
• the Houses We Have Thier Prices lies in between 8 to 40 Lacs
• the house we have list price is 5 lacs
• the house we have maximum price is 50 lacs
• the house we have avarage price is 22.5 lacs

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Crime_Rate

# find the minimum values
df_num.Crime_Rate.min()

0.00632

# find the maximum values
df_num.Crime_Rate.max()

8.98296

# find the average values
df_num.Crime_Rate.mean()

1.2156331382978725

df_num.Crime_Rate.plot(kind='kde',color='red')
plt.title('kde Plot of Crime_Rate')
plt.grid()
plt.show()

Interpretation

• This Data slidely Normaly Distributated
• the house we have list Crime Rate is 0.00632
• the house we have maximum Crime Rate is 88.9762
• the house we have avarage Crime Rate is 3.6
• the Houses We Have Thier Crime Rate lies in between 0.00632 to 25

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Resid_Area

# find the minimum values
df_num.Resid_Area.min()

30.74

# find the maximum values
df_num.Resid_Area.max()

57.74

# find the average values
df_num.Resid_Area.mean()

40.443617021276594

df_num.Resid_Area.plot(kind='kde',color='red')
plt.title('kde Plot of Resid_Area')
plt.grid()
plt.show()

Interpretation

• This Data multi model Distributated
• the house we have list Resid_Area is 30.46
• the house we have maximum Resid_Area is 88.9762
• the house we have avarage Resid_Area is 41.1
• 1 st model the Houses We Have Thier Resid_Area lies in between 25 to 45
• 2 nd model the Houses We Have Thier Resid_Area lies in between 45 to 60

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Air_Quality

# find the minimum values
df_num.Air_Quality.min()

0.385

# find the maximum values
df_num.Air_Quality.max()

0.871

# find the average values
df_num.Air_Quality.mean()

0.5334140957446809

df_num.Air_Quality.plot(kind='kde',color='red')
plt.title('kde Plot of Air_Quality')
plt.grid()
plt.show()

Interpretatio

• This Data slitly normaly Distributated
• the house we have list Air_Quality is 0.385
• the house we have maximum Air_Quality is 0.871
• the house we have avarage Air_Quality is 0.555

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Age

# find the minimum values
df_num.Age.min()

2

# find the maximum values
df_num.Age.max()

100

# find the average values
df_num.Age.mean()

63.922872340425535

df_num.Age.plot(kind='kde',color='black')
plt.title('kde Plot of Age')
plt.grid()
plt.show()

Interpretation

• This Data left skeud Distributated
• the house we have list age is 2
• the house we have maximum age is 100
• the house we have avarage age is 68.04

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Dist1

# find the minimum values
df_num.Dist1.min()

1.34

# find the maximum values
df_num.Dist1.max()

9.44

# find the average values
df_num.Dist1.mean()

4.3179521276595745

df_num.Dist1.plot(kind='kde',color='black')
plt.title('kde Plot of Dist1')
plt.grid()
plt.show()

Interpretation

• We have minimum Distence of the industriyal area is 1.13 km
• we have maximum Distence od the industriyal area is 12.32 km
• we have average Distence of the industriyal area is 3.98 km

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Dist2

# find the minimum values
df_num.Dist2.min()

1.04

# find the maximum values
df_num.Dist2.max()

9.11

# find the average values
df_num.Dist2.mean()

3.9738297872340427

df_num.Dist2.plot(kind='kde',color='black')
plt.title('kde Plot of Dist2')
plt.grid()
plt.show()

Interpretation

• We have minimum Distence of the industriyal area is 0.92 km
• we have maximum Distence od the industriyal area is 11.32 km
• we have average Distence of the industriyal area is 3.68 km

Dist3

# find the minimum values
df_num.Dist3.min()

1.55

# find the maximum values
df_num.Dist3.max()

9.48

# find the average values
df_num.Dist2.mean()

3.9738297872340427

df_num.Dist3.plot(kind='kde',color='black')
plt.title('kde Plot of Dist3')
plt.grid()
plt.show()

Interpreatation

• We have minimum Distence of the industriyal area is 1.15 km
• we have maximum Distence od the industriyal area is 12.32 km
• we have average Distence of the industriyal area is 3.68 km

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Dist4

# find the minimum values
df_num.Dist4.min()

1.05

# find the maximum values
df_num.Dist4.max()

9.16

# find the avarage values
df_num.Dist4.mean()

3.9608244680851064

df_num.Dist4.plot(kind='kde',color='black')
plt.title('kde Plot of Dist4')
plt.grid()
plt.show()

Interpreatation

• We have minimum Distence of the industriyal area is 0.73 km
• we have maximum Distence od the industriyal area is 11.94 km
• we have average Distence of the industriyal area is 3.68 km

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Teachers

# find the minimum values
df_num.Teachers.min()

18.8

# find the maximum values
df_num.Teachers.max()

25.3

# find the average values
df_num.Teachers.mean()

21.4718085106383

df_num.Teachers.plot(kind='kde',color='black')
plt.title('kde Plot of Teachers')
plt.grid()
plt.show()

Interpretation

• we have minimum ratio of teachers around the house is 18
• we have maximum ratio of teachers around the house is 27.4
• we have average ratio of teachers around the house is 21.54

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Poor_Population

# find the minimum values
df_num.Poor_Population.min()

1.98

# find the maximum values
df_num.Poor_Population.max()

30.81

# find the average values
df_num.Poor_Population.mean()

11.774973404255318

df_num.Poor_Population.plot(kind='kde',color='black')
plt.title('kde Plot of Poor_Population')
plt.grid()
plt.show()

Interpretation

• we have ratio of minium poor population is 1.73
• we have ratio of maximum poor population is 37.97
• we have ratio of average poor population is 12.59
• we have ratio of poor population lies between 0.5 to 40

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

N_Hos_Beds

# find the minimum values
df_num.N_Hos_Beds.min()

5.268

# find the maximum values
df_num.N_Hos_Beds.max()

10.668

# find the average values
df_num.N_Hos_Beds.mean()

7.876297872340427

df_num.N_Hos_Beds.plot(kind='kde',color='blue')
plt.title('kde Plot of N_Hos_Beds')
plt.grid()
plt.show()

Interpritation

• This data is multi model distributed
• There houses we have list N_Hos_Beds is 5.268
• The house we have with maximum N_Hos_Beds is 10.876
• The house with average N_Hos_Beds is 7.899767068273099
• 1st The house with the N_Hos_Beds lies between 4.1 and 7.5
• 2nd The house with the N_Hos_Beds lies between 7.5 and 11.6

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

N_Hot_Rooms

# find the minimum values
df_num.N_Hot_Rooms.min()

10.0

# find the maximum values
df_num.N_Hot_Rooms.max()

15.0

# find the average values
df_num.N_Hot_Rooms.mean()

12.5

df_num.N_Hot_Rooms.plot(kind='kde',color='blue')
plt.title('kde Plot of N_Hot_Rooms')
plt.grid()
plt.show()

Interpritation

• This data is single model distributed
• There houses we have list N_Hot_Rooms is 10
• The house we have with maximum N_Hot_Rooms is 101
• The house with average N_Hot_Rooms is 12.867469879518072
• The house with the N_Hot_Rooms lies between 1 and 22

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Rainfall

# find the minimum values
df_num.Rainfall.min()

3

# find the maximum values
df_num.Rainfall.max()

60

# find the average values
df_num.Rainfall.mean()

38.598404255319146

df_num.Rainfall.plot(kind='kde',color='blue')
plt.title('kde Plot of Rainfall')
plt.grid()
plt.show()

Interpritation

• This data is multi model distributed
• There houses we have list Rainfall is 3
• The house we have with maximum Rainfall is 60
• The house with average Rainfall is 39.30522088353413
• 1st The house with the Rainfall lies between 10 and 30
• 2nd The house with the Rainfall lies between 30 and 50
• 3nd The house with the Rainfall lies between 50 and 70

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

Parks

# find the minimum values
df_num.Parks.min()

0.033291762

# find the maximum values
df_num.Parks.max()

0.083032787

# find the Average values
df_num.Parks.mean()

0.05268840343882978

df_num.Parks.plot(kind='kde',color='blue')
plt.title('kde Plot of Parks')
plt.grid()
plt.show()

Interpritation

• This data is single model distributed
• There houses we have list Parks is 0.033291762
• The house we have with maximum Parks is 0.086711473
• The house with average Parks is 0.05436170961044177
• The house with the Parks lies between 0.03 and 0.08

df_num.head()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
0	0	24.0	0.00632	32.31	0.538	65	4.35	3.81	4.18	4.01	24.7	4.98	5.480	11.0	23	0.049347	24.0	0
1	1	21.6	0.02731	37.07	0.469	78	4.99	4.70	5.12	5.06	22.2	9.14	7.332	12.0	42	0.046146	22.0	1
3	3	33.4	0.03237	32.18	0.458	45	6.21	5.93	6.16	5.96	21.3	2.94	9.268	11.0	45	0.047151	21.0	3
4	4	36.2	0.06905	32.18	0.458	54	6.16	5.86	6.37	5.86	21.3	5.33	8.824	11.0	55	0.039474	21.0	4
5	5	28.7	0.02985	32.18	0.458	58	6.22	5.80	6.23	5.99	21.3	5.21	7.174	14.0	53	0.045910	21.0	5

df_cat.head()

	Room_Num	Airport	Waterbody	Bus_Terminal
0	6	YES	River	YES
1	6	NO	Lake	YES
3	6	YES	Lake	YES
4	7	NO	Lake	YES
5	6	YES	None	YES

Bivariant Analysis

# step1: do the Bivariant Analysis on Numarical Data 
# find the corelation
df_num.corr()

	index	Price	Crime_Rate	Resid_Area	Air_Quality	Age	Dist1	Dist2	Dist3	Dist4	Teachers	Poor_Population	N_Hos_Beds	N_Hot_Rooms	Rainfall	Parks	Teachers	index
index	1.000000	-0.130497	0.521741	0.326491	0.322834	0.081943	-0.228977	-0.233908	-0.236092	-0.220572	-0.200740	0.074148	0.058926	0.080481	0.028077	0.301590	-0.261536	1.000000
Price	-0.130497	1.000000	-0.424735	-0.539404	-0.518091	-0.556358	0.368112	0.365491	0.364496	0.366804	0.398508	-0.750503	0.037783	-0.070135	-0.007160	-0.491044	0.402793	-0.130497
Crime_Rate	0.521741	-0.424735	1.000000	0.554473	0.649419	0.418289	-0.456078	-0.455079	-0.458358	-0.447356	-0.302125	0.393205	-0.022331	0.047310	0.026116	0.595869	-0.348126	0.521741
Resid_Area	0.326491	-0.539404	0.554473	1.000000	0.753000	0.598148	-0.700206	-0.703032	-0.703908	-0.702769	-0.289787	0.562430	0.011938	-0.042359	0.014066	0.695053	-0.341594	0.326491
Air_Quality	0.322834	-0.518091	0.649419	0.753000	1.000000	0.688243	-0.745603	-0.747011	-0.746911	-0.742947	-0.189400	0.539936	-0.047659	0.035111	0.027494	0.904715	-0.224335	0.322834
Age	0.081943	-0.556358	0.418289	0.598148	0.688243	1.000000	-0.690885	-0.690063	-0.690537	-0.692938	-0.275263	0.638895	-0.000220	0.003201	0.009894	0.643731	-0.281424	0.081943
Dist1	-0.228977	0.368112	-0.456078	-0.700206	-0.745603	-0.690885	1.000000	0.997494	0.997356	0.993068	0.272658	-0.469418	-0.031694	0.005985	0.028576	-0.692236	0.290859	-0.228977
Dist2	-0.233908	0.365491	-0.455079	-0.703032	-0.747011	-0.690063	0.997494	1.000000	0.997741	0.992902	0.278089	-0.466593	-0.035692	0.008342	0.023889	-0.694414	0.297077	-0.233908
Dist3	-0.236092	0.364496	-0.458358	-0.703908	-0.746911	-0.690537	0.997356	0.997741	1.000000	0.993118	0.276678	-0.465286	-0.029549	0.009390	0.022670	-0.697132	0.295347	-0.236092
Dist4	-0.220572	0.366804	-0.447356	-0.702769	-0.742947	-0.692938	0.993068	0.992902	0.993118	1.000000	0.270843	-0.471031	-0.023569	0.016509	0.034504	-0.692037	0.288949	-0.220572
Teachers	-0.200740	0.398508	-0.302125	-0.289787	-0.189400	-0.275263	0.272658	0.278089	0.276678	0.270843	1.000000	-0.250510	-0.026724	-0.071389	-0.029864	-0.180236	0.988189	-0.200740
Poor_Population	0.074148	-0.750503	0.393205	0.562430	0.539936	0.638895	-0.469418	-0.466593	-0.465286	-0.471031	-0.250510	1.000000	-0.051604	-0.002933	0.001505	0.528261	-0.266741	0.074148
N_Hos_Beds	0.058926	0.037783	-0.022331	0.011938	-0.047659	-0.000220	-0.031694	-0.035692	-0.029549	-0.023569	-0.026724	-0.051604	1.000000	-0.028647	0.066010	-0.077238	-0.036978	0.058926
N_Hot_Rooms	0.080481	-0.070135	0.047310	-0.042359	0.035111	0.003201	0.005985	0.008342	0.009390	0.016509	-0.071389	-0.002933	-0.028647	1.000000	0.078927	0.063127	-0.066646	0.080481
Rainfall	0.028077	-0.007160	0.026116	0.014066	0.027494	0.009894	0.028576	0.023889	0.022670	0.034504	-0.029864	0.001505	0.066010	0.078927	1.000000	0.032996	-0.041106	0.028077
Parks	0.301590	-0.491044	0.595869	0.695053	0.904715	0.643731	-0.692236	-0.694414	-0.697132	-0.692037	-0.180236	0.528261	-0.077238	0.063127	0.032996	1.000000	-0.212599	0.301590
Teachers	-0.261536	0.402793	-0.348126	-0.341594	-0.224335	-0.281424	0.290859	0.297077	0.295347	0.288949	0.988189	-0.266741	-0.036978	-0.066646	-0.041106	-0.212599	1.000000	-0.261536
index	1.000000	-0.130497	0.521741	0.326491	0.322834	0.081943	-0.228977	-0.233908	-0.236092	-0.220572	-0.200740	0.074148	0.058926	0.080481	0.028077	0.301590	-0.261536	1.000000

# Draw the hit map for coralation 
sns.heatmap(df_num.corr(),annot=True)
sns.set(rc={"figure.figsize":(10,10)})

# Dist1 - Parks
# Dist2 - Parks
# Dist3 - Parks
# Dist4 - Parks
# Price - Poor_Population
# Resid_Area - Dist1
# Resid_Area - Dist2
# Resid_Area - Dist3
# Resid_Area - Dist4
# Air_Quality - Dist1
# Air_Quality - Dist2
# Air_Quality - Dist3
# Air_Quality - Dist4
# Age - Dist1
# Age - Dist2
# Age - Dist3
# Age - Dist4
# Resid_Area - Air_Quality
# Age - Air_Quality
# Parks - Resid_Area
# Air_Quality - Parks

Dist1 - Parks

sns.scatterplot(x='Dist1',y='Parks',data=df_num)
plt.xlabel('Dist1',fontsize=30)
plt.ylabel('Parks',fontsize=30)
plt.title('Relation between Dist1 and Parks',fontsize=40)
plt.show()

Interpretation

• There is slitely nagative Corelation

Dist2 - Parks

sns.scatterplot(x='Dist2',y='Parks',data=df_num)
plt.xlabel('Dist2',fontsize=30)
plt.ylabel('Parks',fontsize=30)
plt.title('Relation between Dist2 and Parks',fontsize=40)
plt.show()

Interpretation

• there is slitly negative corelation
• We can say that if Parks of the house then the distens fron Dist2 is lesser

Dist3 - Parks

sns.scatterplot(x='Dist3',y='Parks',data=df_num)
plt.xlabel('Dist3',fontsize=30)
plt.ylabel('Parks',fontsize=30)
plt.title('Relation between Dist3 and Parks',fontsize=40)
plt.show()

Interpretation

-there is slitly negative corelation

Dist4 - Parks

sns.scatterplot(x='Dist4',y='Parks',data=df_num)
plt.xlabel('Dist4',fontsize=30)
plt.ylabel('Parks',fontsize=30)
plt.title('Relation between Dist4 and Parks',fontsize=40)
plt.show()

Price - Poor_Population

sns.scatterplot(x='Price',y='Poor_Population',data=df_num)
plt.xlabel('Price',fontsize=30)
plt.ylabel('Poor_Population',fontsize=30)
plt.title('Relation between Price and Poor_Population',fontsize=40)
plt.show()

Interpretation

-there is slitly negative corelation

Resid_Area - Dist1

sns.scatterplot(x='Resid_Area',y='Dist1',data=df_num)
plt.xlabel('Resid_Area',fontsize=30)
plt.ylabel('Dist1',fontsize=30)
plt.title('Relation between Resid_Area and Dist1',fontsize=40)
plt.show()

Interpretation

• hetrogenues

Resid_Area - Dist2

sns.scatterplot(x='Resid_Area',y='Dist2',data=df_num)
plt.xlabel('Resid_Area',fontsize=30)
plt.ylabel('Dist2',fontsize=30)
plt.title('Relation between Resid_Area and Dist2',fontsize=40)
plt.show()

Resid_Area - Dist3

sns.scatterplot(x='Resid_Area',y='Dist3',data=df_num)
plt.xlabel('Resid_Area',fontsize=30)
plt.ylabel('Dist3',fontsize=30)
plt.title('Relation between Resid_Area and Dist3',fontsize=40)
plt.show()

Resid_Area - Dist4

sns.scatterplot(x='Resid_Area',y='Dist4',data=df_num)
plt.xlabel('Resid_Area',fontsize=30)
plt.ylabel('Dist4',fontsize=30)
plt.title('Relation between Resid_Area and Dist4',fontsize=40)
plt.show()

Air_Quality - Dist1

sns.scatterplot(x='Air_Quality',y='Dist1',data=df_num)
plt.xlabel('Air_Quality',fontsize=30)
plt.ylabel('Dist1',fontsize=30)
plt.title('Relation between Air_Quality and Dist1',fontsize=40)
plt.show()

Air_Quality - Dist2

sns.scatterplot(x='Air_Quality',y='Dist2',data=df_num)
plt.xlabel('Air_Quality',fontsize=30)
plt.ylabel('Dist2',fontsize=30)
plt.title('Relation between Air_Quality and Dist2',fontsize=40)
plt.show()

Air_Quality - Dist3

sns.scatterplot(x='Air_Quality',y='Dist3',data=df_num)
plt.xlabel('Air_Quality',fontsize=30)
plt.ylabel('Dist3',fontsize=30)
plt.title('Relation between Air_Quality and Dist3',fontsize=40)
plt.show()

Air_Quality - Dist4

sns.scatterplot(x='Air_Quality',y='Dist4',data=df_num)
plt.xlabel('Air_Quality',fontsize=30)
plt.ylabel('Dist4',fontsize=30)
plt.title('Relation between Air_Quality and Dist4',fontsize=40)
plt.show()

Age - Dist1

sns.scatterplot(x='Age',y='Dist1',data=df_num)
plt.xlabel('Age',fontsize=30)
plt.ylabel('Dist1',fontsize=30)
plt.title('Relation between Age and Dist1',fontsize=40)
plt.show()

Age - Dist2

sns.scatterplot(x='Age',y='Dist2',data=df_num)
plt.xlabel('Age',fontsize=30)
plt.ylabel('Dist2',fontsize=30)
plt.title('Relation between Age and Dist2',fontsize=40)
plt.show()

Dist3 - Age

sns.scatterplot(x='Dist3',y='Age',data=df_num)
plt.xlabel('Dist3',fontsize=30)
plt.ylabel('Age',fontsize=30)
plt.title('Relation between Dist3 and Age',fontsize=40)
plt.show()

Interpretation

• There is slight posistive Correction
• We can say that if age of the house then the distens fron employeees hub 3 is lesser

Age - Dist4

sns.scatterplot(x='Age',y='Dist4',data=df_num)
plt.xlabel('Age',fontsize=30)
plt.ylabel('Dist4',fontsize=30)
plt.title('Relation between Age and Dist4',fontsize=40)
plt.show()

Resid_Area - Air_Quality

sns.scatterplot(x='Resid_Area',y='Air_Quality',data=df_num)
plt.xlabel('Resid_Area',fontsize=30)
plt.ylabel('Air_Quality',fontsize=30)
plt.title('Relation between Resid_Area and Air_Quality',fontsize=40)
plt.show()

Age - Air_Quality

sns.scatterplot(x='Age',y='Air_Quality',data=df_num)
plt.xlabel('Age',fontsize=30)
plt.ylabel('Air_Quality',fontsize=30)
plt.title('Relation between Age and Air_Quality',fontsize=40)
plt.show()

Parks - Resid_Area

sns.scatterplot(x='Parks',y='Resid_Area',data=df_num)
plt.xlabel('Parks',fontsize=30)
plt.ylabel('Resid_Area',fontsize=30)
plt.title('Relation between Parks and Resid_Area',fontsize=40)
plt.show()

Air_Quality - Parks

sns.scatterplot(x='Air_Quality',y='Parks',data=df_num)
plt.xlabel('Air_Quality',fontsize=30)
plt.ylabel('Parks',fontsize=30)
plt.title('Relation between Air_Quality and Parks',fontsize=40)
plt.show()

Interpretation

• This is Linear Graph

# Parks
# Air_Quality
# Poor_Population
# Price

df_cat.head()

	Room_Num	Airport	Waterbody	Bus_Terminal
0	6	YES	River	YES
1	6	NO	Lake	YES
3	6	YES	Lake	YES
4	7	NO	Lake	YES
5	6	YES	None	YES

x='Room_Num'
y='Parks'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Room_Num',fontsize=20)
plt.ylabel('Parks',fontsize=20)
plt.show()

sns.barplot(x='Room_Num',y='Parks',data=df_house_price_data)
plt.show()

x='Room_Num'
y='Air_Quality'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Room_Num',fontsize=20)
plt.ylabel('Air_Quality',fontsize=20)
plt.show()

x='Room_Num'
y='Poor_Population'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Room_Num',fontsize=20)
plt.ylabel('Poor_Population',fontsize=20)
plt.show()

Interpretatin

• When the proportion of poor population is high , the houses We have over there number of rooms are 5
• if we have 8 number of room , Prpportion pof poor population is low

x='Room_Num'
y='Price'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Room_Num',fontsize=20)
plt.ylabel('Price',fontsize=20)
plt.show()

Interpretation

• when we have best air quality in the area that time the average room number is 4 in the house
• When we have a bed air quallity inthe area that time the average room number is 7 in the house

x='Airport'
y='Parks'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Room_Num',fontsize=20)
plt.ylabel('Parks',fontsize=20)
plt.show()

Interpretation

• nothing the change special

 x='Airport'
y='Air_Quality'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Airport',fontsize=20)
plt.ylabel('Air_Quality',fontsize=20)
plt.show()

Interpretation

• nothing the change special

 x='Airport'
y='Poor_Population'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Airport',fontsize=20)
plt.ylabel('Poor_Population',fontsize=20)
plt.show()

Interpretation

• nothing the change special

 x='Airport'
y='Price'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Airport',fontsize=20)
plt.ylabel('Price',fontsize=20)
plt.show()

Interpretation

• nothing the change special

x='Waterbody'
y='Parks'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Waterbody',fontsize=20)
plt.ylabel('Parks',fontsize=20)
plt.show()

Interpretation

• nothing the change special

x='Waterbody'
y='Parks'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Waterbody',fontsize=20)
plt.ylabel('Parks',fontsize=20)
plt.show()

Interpretation

• nothing the change special

x='Waterbody'
y='Air_Quality'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Waterbody',fontsize=20)
plt.ylabel('Air_Quality',fontsize=20)
plt.show()

Interpretation

• nothing the change special

x='Waterbody'
y='Poor_Population'
plt.bar(x,y,data=df_house_price_data)
plt.xlabel('Waterbody',fontsize=20)
plt.ylabel('Poor_Population',fontsize=20)
plt.show()

Interpretation

• nothing the change special

Multi varieant Analysis

# Add hue
# cross tab
# Pivot table

# Pairs 1
# col1: Prices, col2:Parks, col3: Room_Num
# col1: Prices, col2:Air_Quality, col3: Room_Num
# col1: Prices, col2:Poor_Population, col3: Room_Num
# col1: Prices, col2:Parks, col3: Airports
# col1: Prices, col2:Air_Quality, col3: Airport
# col1: Prices, col2:Poor_Population, col3: Airport
# col1: Prices, col2:Parks, col3: Waterbody
# col1: Prices, col2:Air_Quality, col3: Waterbody
# col1: Prices, col2:Poor_PoPulation, col3: Waterbody


# Note: my Column 3 should be the hue Parametre

sns.set(rc={"figure.figsize":(10,10)})

df_house_price_data.columns

Index(['index', 'Price', 'Crime_Rate', 'Resid_Area', 'Air_Quality', 'Age',
       'Dist1', 'Dist2', 'Dist3', 'Dist4', 'Teachers', 'Poor_Population',
       'N_Hos_Beds', 'N_Hot_Rooms', 'Rainfall', 'Parks', 'Teachers ', 'index',
       'Room_Num', 'Airport', 'Waterbody'],
      dtype='object')

col1: Price, col2:Parks, col3: Room_Num

# scater Plot
sns.scatterplot(x="Price",y="Parks",hue="Room_Num",data=df_house_price_data)
plt.show()

INTERPRETATION

• When number of Parks high (0.08) that time the Price is low (13 lacs) the average numbers of Room_Num is that senario is 3

• Parks when the no of parks is low (0.04) that time the price of the house is high (36 lacs) in that senario we have average room number is 8.

col1: Prices, col2:Air_Quality, col3: Room_Num

# scater Plot
sns.scatterplot(x="Price",y="Air_Quality",hue="Room_Num",data=df_house_price_data)
plt.show()

**Interpretation

• when the price is low(13 lacs) that time the airquality is really good then average number of room in that senario is 4

• when the price is high (36 lacs)that time the airquality is really bed 0.45 then average number of room in that senario is 8

col1: Prices, col2:Poor_Population, col3: Room_Num

# scater Plot
sns.scatterplot(x="Price",y="Poor_Population",hue="Room_Num",data=df_house_price_data)
plt.show()

**Interpretation

• when the price is low(8 lacs) that time the Poor_Population is 30 then average number of room in that senario is 4

• when the price is high (36 lacs)that time the Poor_Population is 8 then average number of room in that senario is 8

col1: Prices, col2:Parks, col3: Airport

# scater Plot
sns.scatterplot(x="Price",y="Parks",hue="Airport",data=df_house_price_data)
plt.show()

Interpretation

• When we have highest Price 37 lacs Then the no of Parks is 0.045 that time ther is Airports is Present.

• When we have lowest Price 17 lacs Then the no of Parks is 0.08 that time ther is Airports is Present

col1: Prices, col2:Air_Quality, col3: Airport

# scater Plot
sns.scatterplot(x="Price",y="Air_Quality",hue="Airport",data=df_house_price_data)
plt.show()

Interpretation

• When we have highest Price 37 lacs Then the Air_Quality is 0.49 that time ther is Airports is Present.

• When we have lowest Price 17 lacs Then the Air_Quality is 0.9 that time ther is Airports is Present

col1: Prices, col2:Poor_Population, col3: Airport

# scater Plot
sns.scatterplot(x="Price",y="Poor_Population",hue="Airport",data=df_house_price_data)
plt.show()

Interpretation

• When we have highest Price 37 lacs Then the Poor_Population is 3 that time ther is Airports is Present.

• When we have lowest Price 8 lacs Then the Poor_Population is 28 that time ther is Airports is Present

col1: Prices, col2:Parks, col3: Waterbody

# scater Plot
sns.scatterplot(x="Price",y="Parks",hue="Waterbody",data=df_house_price_data)
plt.show()

interpretation

• when we have highest price (37 lacs) then the no. of Parks is (0.03) and that time we have river has waterbody.
• when we have lowest price (12 lacs) then the no. of Parks is (0.08) and that time we have river has waterbody.

col1: Prices, col2:Air_Quality, col3: Waterbody

# scater Plot
sns.scatterplot(x="Price",y="Air_Quality",hue="Waterbody",data=df_house_price_data)
plt.show()

interpretation

• when we have highest price (37 lacs) then the Air_Quality is (0.6) and that time we have river has waterbody.
• when we have lowest price (12 lacs) then the Air_Quality is (0.41) and that time we have river has waterbody.

col1: Prices, col2:Poor_Population, col3: Waterbody

# scater Plot
sns.scatterplot(x="Price",y="Poor_Population",hue="Waterbody",data=df_house_price_data)
plt.show()

interpretation

• when we have highest price (32 lacs) then the Poor_Population is (3) and that time we have river has waterbody.
• when we have lowest price (12 lacs) then the Poor_Population is (21) and that time we have river has waterbody.

# Room_Num - Parks
# Room_Num - Air_Quality
# Room_Num - Poor_Populatin
# Airport - Parks
# Airport - Air_Quality
# Airport - Poor_Populatin
# Waterbody - Parks
# Waterbody - Air_Quality
# Waterbody - Poor_Populatin

# col1:Room_Num, col2: Airporat, col3: Price
# col1:Room_Num, col2: Waterbody, col3: Price
# col1:Airport, col2: Waterbody, col3: Price

col1:Room_Num, col2: Airporat, col3: Price

df_house_price_data.columns

Index(['index', 'Price', 'Crime_Rate', 'Resid_Area', 'Air_Quality', 'Age',
       'Dist1', 'Dist2', 'Dist3', 'Dist4', 'Teachers', 'Poor_Population',
       'N_Hos_Beds', 'N_Hot_Rooms', 'Rainfall', 'Parks', 'Teachers ', 'index',
       'Room_Num', 'Airport', 'Waterbody'],
      dtype='object')

Cross tab

pd.crosstab(df_house_price_data.Room_Num,df_house_price_data.Airport,values=df_house_price_data.Price,aggfunc='mean')

Airport	NO	YES
Room_Num
3	NaN	27.500000
4	17.533333	11.800000
5	17.485000	19.328333
6	22.522449	22.969231
7	31.855556	31.684615
8	21.900000	NaN

Interpretation

• We can observerd at average Room_Num are 3 Airport are present and Average price is 27.5 lacs
• We can observerd at average Room_Num are 7 Airport are present and Average price is 31.68 lacs
• We can observerd at average Room_Num are 7 Airport arenot present and Average price is 31.85 lacs
• we can observed at average Room_Num are 8 Airport are not present and avrage price is 21.9 lacs

col1:Room_Num, col2: Waterbody, col3: Price

pd.crosstab(df_house_price_data.Room_Num,df_house_price_data.Waterbody,values=df_house_price_data.Price,aggfunc='mean')

Waterbody	Lake	Lake and River	None	River
Room_Num
3	NaN	NaN	NaN	27.500000
4	21.900000	NaN	14.600000	13.950000
5	18.383333	18.200000	18.150000	18.757500
6	22.871795	22.057143	22.574286	23.202381
7	32.116667	32.600000	31.033333	31.600000
8	NaN	NaN	NaN	21.900000

Interpretation

• We can observed Average room numbers 3 and waterbody as river present at House price is 27.5 lacs
• we can observed Average room numbers 5 and waterbody as river, Lake and River, river present at house average house price is 18.37 lacs
• we can observed Average room numbers 7 and waterbody as river, Lake and River, river present at house average house price is 31.25 lacs
• We can observed Average room numbers 8 and waterbody as river present at House price is 21.9 lacs

col1:Airport, col2: Waterbody, col3: Price

pd.crosstab(df_house_price_data.Airport,df_house_price_data.Waterbody,values=df_house_price_data.Price,aggfunc='mean')

Waterbody	Lake	Lake and River	None	River
Airport
NO	21.385714	20.003226	20.655319	21.966154
YES	22.595238	22.695833	21.643077	22.924324

Interpretation

• We can observed At airport is not present but lake as waterbody is present at average house price is 21.38 lacs
• We can observed At airport is not present but lake and river as waterbody is present at average house price is 20 lacs
• We can observed At airport is not present but River as waterbody is present at average house price is 21.96 lacs
• we can observed at airport is present and waterbody as lake and river present at average house price is 22.69 lacs
• We can observed At airport is present but Lake as waterbody is present at average house price is 22.59 lacs

pivot_table

col1:Airport, col2: Waterbody, col3: Price

pd.pivot_table(data=df_house_price_data,
               values='Price',
               index='Airport',
               columns='Waterbody',
               aggfunc='mean')

Waterbody	Lake	Lake and River	None	River
Airport
NO	21.385714	20.003226	20.655319	21.966154
YES	22.595238	22.695833	21.643077	22.924324

Interpretation

• We can observed At airport is not present but lake as waterbody is present at average house price is 21.38 lacs
• We can observed At airport is not present but lake and river as waterbody is present at average house price is 20 lacs
• We can observed At airport is not present but River as waterbody is present at average house price is 21.96 lacs
• we can observed at airport is present and waterbody as lake and river present at average house price is 22.69 lacs
• We can observed At airport is present but Lake as waterbody is present at average house price is 22.59 lacs

col1:Room_Num, col2: Waterbody, col3: Price

pd.pivot_table(data=df_house_price_data,
               values='Price',
               index='Room_Num',
               columns='Waterbody',
               aggfunc='mean')

Waterbody	Lake	Lake and River	None	River
Room_Num
3	NaN	NaN	NaN	27.500000
4	21.900000	NaN	14.600000	13.950000
5	18.383333	18.200000	18.150000	18.757500
6	22.871795	22.057143	22.574286	23.202381
7	32.116667	32.600000	31.033333	31.600000
8	NaN	NaN	NaN	21.900000

Interpretation

• We can observed At airport is not present but lake as waterbody is present at average house price is 21.38 lacs
• We can observed At airport is not present but lake and river as waterbody is present at average house price is 20 lacs
• We can observed At airport is not present but River as waterbody is present at average house price is 21.96 lacs
• we can observed at airport is present and waterbody as lake and river present at average house price is 22.69 lacs
• We can observed At airport is present but Lake as waterbody is present at average house price is 22.59 lacs

col1:Room_Num, col2: Airporat, col3: Price

pd.pivot_table(data=df_house_price_data,
               values='Price',
               index='Room_Num',
               columns='Airport',
               aggfunc='mean')

Airport	NO	YES
Room_Num
3	NaN	27.500000
4	17.533333	11.800000
5	17.485000	19.328333
6	22.522449	22.969231
7	31.855556	31.684615
8	21.900000	NaN

Interpretation

• We can observerd at average Room_Num are 3 Airport are present and Average price is 27.5 lacs
• We can observerd at average Room_Num are 7 Airport are present and Average price is 31.68 lacs
• We can observerd at average Room_Num are 7 Airport arenot present and Average price is 31.85 lacs
• we can observed at average Room_Num are 8 Airport are not present and avrage price is 21.9 lacs